Load data from All.RData

rm(list=ls())  # clean up workspace
load("/Users/xji3/GitFolders/IGCCodonSimulation/All.RData")

# paml.path <- "/Users/xji3/GitFolders/IGCCodonSimulation/"
# IGC.geo.list <- c(3.0)
# 
# # Read in new PAML results
# data.path <- paste(paralog, "",sep = "/")
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "estimatedTau", "paml", "unrooted", "1stTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "estimatedTau", paste(toString(IGC.geo), ".0", sep = ""), "1stTree", "summary", sep = "_"), summary_mat)
# }
# 
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "estimatedTau", "paml", "unrooted", "2ndTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "estimatedTau", paste(toString(IGC.geo), ".0", sep = ""), "2ndTree", "summary", sep = "_"), summary_mat)
# }
# 
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "10Tau", "paml", "unrooted", "1stTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "10Tau", paste(toString(IGC.geo), ".0", sep = ""), "1stTree", "summary", sep = "_"), summary_mat)
#   }
# 
# for (IGC.geo in IGC.geo.list){
#   summary_mat <- NULL
#   IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
#   file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "10Tau", "paml", "unrooted", "2ndTree", "summary.txt", sep = "_")
#   for (sim.num in 0:(num.sim - 1)){
#     summary_file <- paste(paml.path, file.name, sep = "")
#     if (file.exists(summary_file)){
#       all <- readLines(summary_file, n = -1)
#       col.names <- strsplit(all[1], ' ')[[1]][-1]
#       row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
#       summary_mat <- as.matrix(read.table(summary_file, 
#                                           row.names = row.names, 
#                                           col.names = col.names))
#       
#       }
#     }
#   assign(paste("PAML", "10Tau", paste(toString(IGC.geo), ".0", sep = ""), "2ndTree", "summary", sep = "_"), summary_mat)
# }

save.image("/Users/xji3/GitFolders/IGCCodonSimulation/All.RData")

Feb 10 update

non.zero.datasets <- (PAML_estimatedTau_3.0_1stTree_summary[4, ] > 1e-5 & PAML_estimatedTau_3.0_1stTree_summary[6,] > 1e-5)
# Re-examine N4_N5, N4_mikatae
IGC.geo.list <- c(3.0, 10.0, 50.0, 100.0, 500.0)

# N4_N5
non.zero.PAML.N4.N5.paralog1.mean.list <- c(mean(PAML_3.0_summary["N4_N5", non.zero.datasets]), 
                                   mean(PAML_10.0_summary["N4_N5", non.zero.datasets]),
                                   mean(PAML_50.0_summary["N4_N5", non.zero.datasets]),
                                   mean(PAML_100.0_summary["N4_N5", non.zero.datasets]),
                                   mean(PAML_500.0_summary["N4_N5", non.zero.datasets]))
non.zero.PAML.N4.N5.paralog1.sd.list <- c(sd(PAML_3.0_summary["N4_N5", non.zero.datasets]), 
                                 sd(PAML_10.0_summary["N4_N5", non.zero.datasets]),
                                 sd(PAML_50.0_summary["N4_N5", non.zero.datasets]),
                                 sd(PAML_100.0_summary["N4_N5", non.zero.datasets]),
                                 sd(PAML_500.0_summary["N4_N5", non.zero.datasets]))
non.zero.PAML.N4.N5.paralog2.mean.list <- c(mean(PAML_3.0_summary["N9_N10", non.zero.datasets]), 
                                   mean(PAML_10.0_summary["N9_N10", non.zero.datasets]),
                                   mean(PAML_50.0_summary["N9_N10", non.zero.datasets]),
                                   mean(PAML_100.0_summary["N9_N10", non.zero.datasets]),
                                   mean(PAML_500.0_summary["N9_N10", non.zero.datasets]))
non.zero.PAML.N4.N5.paralog2.sd.list <- c(sd(PAML_3.0_summary["N9_N10", non.zero.datasets]), 
                                 sd(PAML_10.0_summary["N9_N10", non.zero.datasets]),
                                 sd(PAML_50.0_summary["N9_N10", non.zero.datasets]),
                                 sd(PAML_100.0_summary["N9_N10", non.zero.datasets]),
                                 sd(PAML_500.0_summary["N9_N10", non.zero.datasets]))
# N4_mikatae
non.zero.PAML.N4.mikatae.paralog1.mean.list <- c(mean(PAML_3.0_summary["N4_mikataeYDR418W", non.zero.datasets]), 
                                        mean(PAML_10.0_summary["N4_mikataeYDR418W", non.zero.datasets]),
                                        mean(PAML_50.0_summary["N4_mikataeYDR418W", non.zero.datasets]),
                                        mean(PAML_100.0_summary["N4_mikataeYDR418W", non.zero.datasets]),
                                        mean(PAML_500.0_summary["N4_mikataeYDR418W", non.zero.datasets]))
non.zero.PAML.N4.mikatae.paralog1.sd.list <- c(sd(PAML_3.0_summary["N4_mikataeYDR418W", non.zero.datasets]), 
                                      sd(PAML_10.0_summary["N4_mikataeYDR418W", non.zero.datasets]),
                                      sd(PAML_50.0_summary["N4_mikataeYDR418W", non.zero.datasets]),
                                      sd(PAML_100.0_summary["N4_mikataeYDR418W", non.zero.datasets]),
                                      sd(PAML_500.0_summary["N4_mikataeYDR418W", non.zero.datasets]))
non.zero.PAML.N4.mikatae.paralog2.mean.list <- c(mean(PAML_3.0_summary["N9_mikataeYEL054C", non.zero.datasets]), 
                                        mean(PAML_10.0_summary["N9_mikataeYEL054C", non.zero.datasets]),
                                        mean(PAML_50.0_summary["N9_mikataeYEL054C", non.zero.datasets]),
                                        mean(PAML_100.0_summary["N9_mikataeYEL054C", non.zero.datasets]),
                                        mean(PAML_500.0_summary["N9_mikataeYEL054C", non.zero.datasets]))
non.zero.PAML.N4.mikatae.paralog2.sd.list <- c(sd(PAML_3.0_summary["N9_mikataeYEL054C", non.zero.datasets]), 
                                      sd(PAML_10.0_summary["N9_mikataeYEL054C", non.zero.datasets]),
                                      sd(PAML_50.0_summary["N9_mikataeYEL054C", non.zero.datasets]),
                                      sd(PAML_100.0_summary["N9_mikataeYEL054C", non.zero.datasets]),
                                      sd(PAML_500.0_summary["N9_mikataeYEL054C", non.zero.datasets]))

# N4_N5
par(mfrow = c(2, 2))
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.N5.paralog1.mean.list, non.zero.PAML.N4.N5.paralog2.mean.list, mean.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean non.zero N4.N5 estimate")
abline(h = 0.024947887926)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.N5.paralog1.sd.list, non.zero.PAML.N4.N5.paralog2.sd.list, sd.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd non.zero N4.N5 estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)

# N4_N5
matplot(IGC.geo.list, cbind(PAML.N4.N5.paralog1.mean.list, PAML.N4.N5.paralog2.mean.list, mean.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean N4.N5 estimate")
abline(h = 0.024947887926)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(PAML.N4.N5.paralog1.sd.list, PAML.N4.N5.paralog2.sd.list, sd.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd N4.N5 estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)

plot of chunk unnamed-chunk-2

par(mfrow = c(2, 2))
# N4_mikatae
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.N5.paralog1.mean.list, non.zero.PAML.N4.mikatae.paralog2.mean.list, mean.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean non.zero N4.mikatae estimate")
abline(h = 0.0566627496729)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.mikatae.paralog1.sd.list, non.zero.PAML.N4.mikatae.paralog2.sd.list, sd.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd non.zero N4.mikatae estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
# N4_mikatae
matplot(IGC.geo.list, cbind(PAML.N4.N5.paralog1.mean.list, PAML.N4.mikatae.paralog2.mean.list, mean.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean N4.mikatae estimate")
abline(h = 0.0566627496729)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(PAML.N4.mikatae.paralog1.sd.list, PAML.N4.mikatae.paralog2.sd.list, sd.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd N4.mikatae estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)

plot of chunk unnamed-chunk-2

Feb 5 update

Check PAML estimate of two same tree topologies but different order of taxa

# Simulation using estimated Tau value of 1.409408
# Look at difference of each estimate (Tree 1 - Tree 2)

# log likelihood
summary(PAML_estimatedTau_3.0_1stTree_summary["ll", ] - PAML_estimatedTau_3.0_2ndTree_summary["ll", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0e+00   0e+00   0e+00   1e-08   0e+00   1e-06
sd(PAML_estimatedTau_3.0_1stTree_summary["ll", ] - PAML_estimatedTau_3.0_2ndTree_summary["ll", ])
## [1] 1e-07
# kappa
summary(PAML_estimatedTau_3.0_1stTree_summary["kappa", ] - PAML_estimatedTau_3.0_2ndTree_summary["kappa", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -2e-05   0e+00   0e+00  -1e-07   0e+00   1e-05
sd(PAML_estimatedTau_3.0_1stTree_summary["kappa", ] - PAML_estimatedTau_3.0_2ndTree_summary["kappa", ])
## [1] 5.221e-06
# omega
summary(PAML_estimatedTau_3.0_1stTree_summary["omega", ] - PAML_estimatedTau_3.0_2ndTree_summary["omega", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -1e-05   0e+00   0e+00  -3e-07   0e+00   0e+00
sd(PAML_estimatedTau_3.0_1stTree_summary["omega", ] - PAML_estimatedTau_3.0_2ndTree_summary["omega", ])
## [1] 1.714e-06
# N0_kluyveriYDR418W
summary(PAML_estimatedTau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -5e-06   0e+00   0e+00  -1e-08   0e+00   1e-06
sd(PAML_estimatedTau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
## [1] 5.773e-07
# N0_N1
summary(PAML_estimatedTau_3.0_1stTree_summary["N0_N1", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N6", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -1e-06   0e+00   0e+00  -1e-08   0e+00   0e+00
sd(PAML_estimatedTau_3.0_1stTree_summary["N0_N1", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N6", ])
## [1] 1e-07

There seems no difference between two different tree representations

Now check PAML results on simulation with 10*Tau

# Simulation using 10 * Tau value of 1.409408 * 10 = 14.09408
# Look at difference of each estimate (Tree 1 - Tree 2)

# log likelihood
summary(PAML_10Tau_3.0_1stTree_summary["ll", ] - PAML_10Tau_3.0_2ndTree_summary["ll", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -7.740   0.000   0.000  -0.225   0.000   5.200
sd(PAML_10Tau_3.0_1stTree_summary["ll", ] - PAML_10Tau_3.0_2ndTree_summary["ll", ])
## [1] 1.692
hist(PAML_10Tau_3.0_1stTree_summary["ll", ] - PAML_10Tau_3.0_2ndTree_summary["ll", ])

plot of chunk unnamed-chunk-4

# kappa
summary(PAML_10Tau_3.0_1stTree_summary["kappa", ] - PAML_10Tau_3.0_2ndTree_summary["kappa", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.9910  0.0000  0.0000 -0.0663  0.0000  0.1990
sd(PAML_10Tau_3.0_1stTree_summary["kappa", ] - PAML_10Tau_3.0_2ndTree_summary["kappa", ])
## [1] 0.1903
# omega
summary(PAML_10Tau_3.0_1stTree_summary["omega", ] - PAML_10Tau_3.0_2ndTree_summary["omega", ])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.13200  0.00000  0.00000 -0.00004  0.00000  0.05890
sd(PAML_10Tau_3.0_1stTree_summary["omega", ] - PAML_10Tau_3.0_2ndTree_summary["omega", ])
## [1] 0.01823
# N0_kluyveriYDR418W
summary(PAML_10Tau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.1900  0.0000  0.0000 -0.0262  0.0000  0.0000
sd(PAML_10Tau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
## [1] 0.05796
# N0_N1
summary(PAML_10Tau_3.0_1stTree_summary["N0_N1", ] - PAML_10Tau_3.0_2ndTree_summary["N0_N6", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0       0       0       0       0       0
sd(PAML_10Tau_3.0_1stTree_summary["N0_N1", ] - PAML_10Tau_3.0_2ndTree_summary["N0_N6", ])
## [1] 0
# N1_N2
summary(PAML_10Tau_3.0_1stTree_summary["N1_N2", ] - PAML_10Tau_3.0_2ndTree_summary["N6_N7", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.3880  0.0000  0.0000  0.0036  0.0515  0.2340
sd(PAML_10Tau_3.0_1stTree_summary["N1_N2", ] - PAML_10Tau_3.0_2ndTree_summary["N6_N7", ])
## [1] 0.1073
hist(PAML_10Tau_3.0_1stTree_summary["N1_N2", ] - PAML_10Tau_3.0_2ndTree_summary["N6_N7", ])

plot of chunk unnamed-chunk-4

# N1_castelliiYDR418W
summary(PAML_10Tau_3.0_1stTree_summary["N1_castelliiYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N6_castelliiYDR418W", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.2340 -0.0515  0.0000 -0.0068  0.0000  0.3880
sd(PAML_10Tau_3.0_1stTree_summary["N1_castelliiYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N6_castelliiYDR418W", ])
## [1] 0.1068
hist(PAML_10Tau_3.0_1stTree_summary["N1_castelliiYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N6_castelliiYDR418W", ])

plot of chunk unnamed-chunk-4

# N2_N3
summary(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.07610 -0.01730  0.00000 -0.00754  0.00000  0.05470
sd(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
## [1] 0.02132
hist(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
# N2_N3
summary(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.07610 -0.01730  0.00000 -0.00754  0.00000  0.05470
sd(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
## [1] 0.02132
hist(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])

plot of chunk unnamed-chunk-4

# N2_bayanusYDR418W
summary(PAML_10Tau_3.0_1stTree_summary["N2_bayanusYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N7_bayanusYDR418W", ])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.05470 -0.00004  0.00000  0.00712  0.01730  0.07610
sd(PAML_10Tau_3.0_1stTree_summary["N2_bayanusYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N7_bayanusYDR418W", ])
## [1] 0.0208
hist(PAML_10Tau_3.0_1stTree_summary["N2_bayanusYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N7_bayanusYDR418W", ])

plot of chunk unnamed-chunk-4 Now start to check branches ratio of subtree branches of paralog 1 over paralog 2 ratios

# N0_N1
target <- PAML_3.0_summary["N0_N1", ] / PAML_3.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

plot of chunk unnamed-chunk-5

## [1] 682.6
## [1] 1311
# N1_N2
target <- PAML_3.0_summary["N1_N2", ] / PAML_3.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

plot of chunk unnamed-chunk-5

## [1] 1.009
## [1] 0.2219
# N1_castellii
target <- PAML_3.0_summary["N1_castelliiYDR418W", ] / PAML_3.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

plot of chunk unnamed-chunk-5

## [1] 1.01
## [1] 0.2422
# N2_N3
target <- PAML_3.0_summary["N2_N3", ] / PAML_3.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

plot of chunk unnamed-chunk-5

## [1] 396.5
## [1] 1982
# N2_bayanus
target <- PAML_3.0_summary["N2_bayanusYDR418W", ] / PAML_3.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

plot of chunk unnamed-chunk-5

## [1] 1.136
## [1] 0.6685
# N3_N4
target <- PAML_3.0_summary["N3_N4", ] / PAML_3.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

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## [1] 1.501
## [1] 1.811
# N3_kudriavzevii
target <- PAML_3.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_3.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

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## [1] 1.04
## [1] 0.329
# N4_N5
target <- PAML_3.0_summary["N4_N5", ] / PAML_3.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

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## [1] 0.9776
## [1] 1.313
# N4_mikatae
target <- PAML_3.0_summary["N4_mikataeYDR418W", ] / PAML_3.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

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## [1] 1.867
## [1] 1.964
# N5_paradoxus
target <- PAML_3.0_summary["N5_paradoxusYDR418W", ] / PAML_3.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

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## [1] 1.377
## [1] 1.532
# N5_cerevisiae
target <- PAML_3.0_summary["N5_cerevisiaeYDR418W", ] / PAML_3.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

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## [1] 1.042
## [1] 0.502
# N0_N1
target <- PAML_10.0_summary["N0_N1", ] / PAML_10.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

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## [1] 808.2
## [1] 1663
# N1_N2
target <- PAML_10.0_summary["N1_N2", ] / PAML_10.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

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## [1] 1.071
## [1] 0.2817
# N1_castellii
target <- PAML_10.0_summary["N1_castelliiYDR418W", ] / PAML_10.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

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## [1] 1.026
## [1] 0.3345
# N2_N3
target <- PAML_10.0_summary["N2_N3", ] / PAML_10.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

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## [1] 161.1
## [1] 1169
# N2_bayanus
target <- PAML_10.0_summary["N2_bayanusYDR418W", ] / PAML_10.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

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## [1] 32.6
## [1] 314
# N3_N4
target <- PAML_10.0_summary["N3_N4", ] / PAML_10.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

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## [1] 1.538
## [1] 1.69
# N3_kudriavzevii
target <- PAML_10.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_10.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

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## [1] 1.144
## [1] 0.4798
# N4_N5
target <- PAML_10.0_summary["N4_N5", ] / PAML_10.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

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## [1] 211
## [1] 1482
# N4_mikatae
target <- PAML_10.0_summary["N4_mikataeYDR418W", ] / PAML_10.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

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## [1] 113.2
## [1] 1109
# N5_paradoxus
target <- PAML_10.0_summary["N5_paradoxusYDR418W", ] / PAML_10.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

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## [1] 1.291
## [1] 1.113
# N5_cerevisiae
target <- PAML_10.0_summary["N5_cerevisiaeYDR418W", ] / PAML_10.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

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## [1] 1.041
## [1] 0.5688
# N0_N1
target <- PAML_50.0_summary["N0_N1", ] / PAML_50.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

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## [1] 544
## [1] 1427
# N1_N2
target <- PAML_50.0_summary["N1_N2", ] / PAML_50.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

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## [1] 1.073
## [1] 0.2958
# N1_castellii
target <- PAML_50.0_summary["N1_castelliiYDR418W", ] / PAML_50.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

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## [1] 1.021
## [1] 0.2728
# N2_N3
target <- PAML_50.0_summary["N2_N3", ] / PAML_50.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

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## [1] 1.352
## [1] 1.576
# N2_bayanus
target <- PAML_50.0_summary["N2_bayanusYDR418W", ] / PAML_50.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

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## [1] 1.46
## [1] 1.934
# N3_N4
target <- PAML_50.0_summary["N3_N4", ] / PAML_50.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

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## [1] 43.96
## [1] 424.1
# N3_kudriavzevii
target <- PAML_50.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_50.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

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## [1] 1.166
## [1] 0.7124
# N4_N5
target <- PAML_50.0_summary["N4_N5", ] / PAML_50.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

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## [1] 1.407
## [1] 1.702
# N4_mikatae
target <- PAML_50.0_summary["N4_mikataeYDR418W", ] / PAML_50.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

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## [1] 186.1
## [1] 1843
# N5_paradoxus
target <- PAML_50.0_summary["N5_paradoxusYDR418W", ] / PAML_50.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

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## [1] 101.7
## [1] 572.2
# N5_cerevisiae
target <- PAML_50.0_summary["N5_cerevisiaeYDR418W", ] / PAML_50.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

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## [1] 1.336
## [1] 0.9247
 # N0_N1
target <- PAML_100.0_summary["N0_N1", ] / PAML_100.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

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## [1] 702.9
## [1] 1764
# N1_N2
target <- PAML_100.0_summary["N1_N2", ] / PAML_100.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

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## [1] 1.05
## [1] 0.3782
# N1_castellii
target <- PAML_100.0_summary["N1_castelliiYDR418W", ] / PAML_100.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

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## [1] 1.032
## [1] 0.2839
# N2_N3
target <- PAML_100.0_summary["N2_N3", ] / PAML_100.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

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## [1] 89.56
## [1] 539.4
# N2_bayanus
target <- PAML_100.0_summary["N2_bayanusYDR418W", ] / PAML_100.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

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## [1] 1.386
## [1] 1.511
# N3_N4
target <- PAML_100.0_summary["N3_N4", ] / PAML_100.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

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## [1] 888.6
## [1] 5071
# N3_kudriavzevii
target <- PAML_100.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_100.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

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## [1] 1.238
## [1] 0.9739
# N4_N5
target <- PAML_100.0_summary["N4_N5", ] / PAML_100.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

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## [1] 1.446
## [1] 3.041
# N4_mikatae
target <- PAML_100.0_summary["N4_mikataeYDR418W", ] / PAML_100.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

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## [1] 2.462
## [1] 3.709
# N5_paradoxus
target <- PAML_100.0_summary["N5_paradoxusYDR418W", ] / PAML_100.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

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## [1] 845.1
## [1] 7179
# N5_cerevisiae
target <- PAML_100.0_summary["N5_cerevisiaeYDR418W", ] / PAML_100.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

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## [1] 1.472
## [1] 1.873
 # N0_N1
target <- PAML_500.0_summary["N0_N1", ] / PAML_500.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

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## [1] 390.8
## [1] 1293
# N1_N2
target <- PAML_500.0_summary["N1_N2", ] / PAML_500.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

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## [1] 1219
## [1] 8571
# N1_castellii
target <- PAML_500.0_summary["N1_castelliiYDR418W", ] / PAML_500.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

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## [1] 1.067
## [1] 0.2985
# N2_N3
target <- PAML_500.0_summary["N2_N3", ] / PAML_500.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

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## [1] 1309
## [1] 6196
# N2_bayanus
target <- PAML_500.0_summary["N2_bayanusYDR418W", ] / PAML_500.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

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## [1] 547.7
## [1] 4709
# N3_N4
target <- PAML_500.0_summary["N3_N4", ] / PAML_500.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

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## [1] 605.8
## [1] 2710
# N3_kudriavzevii
target <- PAML_500.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_500.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

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## [1] 1.347
## [1] 1.256
# N4_N5
target <- PAML_500.0_summary["N4_N5", ] / PAML_500.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

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## [1] 1018
## [1] 9689
# N4_mikatae
target <- PAML_500.0_summary["N4_mikataeYDR418W", ] / PAML_500.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

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## [1] 722.6
## [1] 3206
# N5_paradoxus
target <- PAML_500.0_summary["N5_paradoxusYDR418W", ] / PAML_500.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

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## [1] 140.7
## [1] 824.2
# N5_cerevisiae
target <- PAML_500.0_summary["N5_cerevisiaeYDR418W", ] / PAML_500.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

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## [1] 1.353
## [1] 1.52